Automated drug delivery (or drug administration under computer guidance or control) can improve drug therapy by allowing for more efficient and smoother delivery. This may reduce drug usage, side effects and costs; permit health care staff to work more efficiently; and allow the safe use of drugs that are difficult to administer manually, leading to better care for the patient. One example is automatic drug delivery, with the development of models and control methods specifically for control of neuromuscular blockade (NMB). NMB drugs produce paralysis to prevent motion, for example to permit tracheal intubation and allow access to deep structures with smaller incisions.
As NMB drugs have high therapeutic indices in hospital settings, they are often used in excess of minimal effective requirements. A strategy for administration is to provide an overdose to prolong paralysis, monitor for returning muscle function and, once it returns, overdose again [1]. The large dose delivers rapid onset of paralysis, quicker surgical conditions, and avoids titration to a precise anesthetic setpoint and regulation once there [2].
Unfortunately this approach can reduce fine control and can increase toxicity. Fine control can be preferable during surgeries where knowledge of the patient's state is important for safety. For example, in Harrington rod insertion for reshaping the spine, the surgeon assesses whether or not the rods have impinged nerves by the ability of the patient to respond physically. Testing can be performed only after the return of muscle function. Under automatic control the patient minimally can be kept minimally paralyzed until a test is required, reduce drug administration to allow function to return, and then re-paralyze for continued work with minimal waiting time by the surgical staff.
The overdosing strategy is also a source of inconvenience if complications arise and the surgical conditions change, which is not uncommon. An example of this was seen in the study used to collect data for the NMB Advisory System (NMBAS) initial patient model [3]. After paralysis and induction of anesthesia had taken place, examination of the patient revealed extensive invasive carcinoma. The procedure was cancelled, and the anesthesiologist and attending nurses monitored the patient until the drug wore off enough that the patient could be reversed.
Computer control of NMB has been attempted previously. Researchers have been able to deliver blockade at a near-constant controlled, setable level compared to conventional practice, while using less drug [1, 4]. Representative efforts include bang-bang [5], Proportional Integral Derivative (PID) control [6], and PID/Smith predictor [7] and fuzzy logic control [8]. Some of the controllers developed have not been stable or robust enough to handle the intra- and interpatient variability present.
Other controllers have achieved near constant levels of blockade in relatively controlled experimental settings, but are associated with significant constraints that thus far have impeded their utility in routine clinical practice. For example, most involve the use of single twitch stimulation to measure response (ST or T1%). In addition to the often considerable associated setup time, the use of single twitch stimulation necessitates a stable control [9] and T1% baseline stabilization requires up to 20 minutes between induction and NMB drug administration [10], unnecessarily exposing patients to the risks of an unprotected airway and creating unacceptable operating room time delays. Furthermore, the typical controller setpoint was T1%=10% (i.e., 90% single twitch suppression) [8, 10, 11, 12] which represents a potentially non-reversible state.
Adaptive control techniques may help accommodate the patient variance. An adaptive controller is a fixed structure controller with adjustable parameters and a mechanism for automatically adjusting those parameters. Adaptive control's roots begin in the 1950s with the development of the autopilot for high-performance aircraft [13]. Since then there has been much theoretical development and application. An in-depth review of this field appears in [13].
An example of an adaptive control technique that has been used not just in chemical batch processes but in clinical application as well is Generalized Predictive Control [14, 15], a general-purpose adaptive control method. This method was used in the operating room in control of NMB as described in [16].